{"id":37886,"date":"2022-12-16T14:37:13","date_gmt":"2022-12-16T14:37:13","guid":{"rendered":"https:\/\/www.askpython.com\/?p=37886"},"modified":"2022-12-16T14:37:14","modified_gmt":"2022-12-16T14:37:14","slug":"numpy-ones","status":"publish","type":"post","link":"https:\/\/www.askpython.com\/python-modules\/numpy\/numpy-ones","title":{"rendered":"NumPy ones – A Complete Guide"},"content":{"rendered":"\n

Hello and welcome to this tutorial on Numpy ones<\/strong>. In this tutorial, we will be learning about the NumPy ones()<\/strong> method and also seeing a lot of examples regarding the same. So let us begin!<\/p>\n\n\n\n

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What is the NumPy ones method?<\/h2>\n\n\n\n

NumPy\u00a0ones<\/code>\u00a0returns a\u00a0Numpy array<\/strong><\/a>\u00a0of the given shape and data type with all values set to 1.<\/p>\n\n\n\n

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Syntax of NumPy ones<\/h2>\n\n\n\n

Let us first have a look at the syntax of NumPy ones.<\/p>\n\n\n

\nnumpy.ones(shape, dtype=None, order='C', like=None)\n<\/pre><\/div>\n\n\n
Parameter<\/strong><\/td>Description<\/strong><\/td>Required\/Optional<\/strong><\/td><\/tr>
shape<\/td>The desired shape of the array. It can be an int or a tuple of ints.<\/td>Required<\/td><\/tr>
dtype<\/td>The desired data type of the array.
The default data type is float<\/strong>.<\/td>		Optional<\/td><\/tr>
order<\/td>The desired order in which the multi-dimensional data is to be stored in the memory. It can either be row-major (\u2018C\u2019) or column-major (\u2018F\u2019).
The default order is \u2018C\u2019<\/strong> i.e. row-major<\/strong>.<\/td>		Optional<\/td><\/tr>
like (array_like)<\/td>Reference object to allow the creation of arrays that are not NumPy arrays.<\/td>Optional<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
Returns:<\/strong> 
An array with the given shape, data type and order filled with only ones.<\/p>\n\n\n\n
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Examples of using NumPy ones<\/h2>\n\n\n\n
Let\u2019s now look at some practical examples of the numpy.ones()<\/code> method.<\/p>\n\n\n\n
1-dimensional array using ones<\/h3>\n\n\n
\nimport numpy as np\n\none_dim_array = np.ones(4)\nprint(one_dim_array) \n<\/pre><\/div>\n\n\n
Output:<\/strong><\/p>\n\n\n
\n[1. 1. 1. 1.]\n<\/pre><\/div>\n\n\n
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2-dimensional array using zeros<\/h3>\n\n\n\n
N x M array<\/strong><\/p>\n\n\n
\nimport numpy as np\n\ntwo_dim_array = np.ones((4, 2))\nprint(two_dim_array) \n<\/pre><\/div>\n\n\n
Output:<\/strong><\/p>\n\n\n
\n[[1. 1.]\n [1. 1.]\n [1. 1.]\n [1. 1.]]\n<\/pre><\/div>\n\n\n
1 x N array<\/strong><\/p>\n\n\n
\nimport numpy as np\n\none_row_array = np.ones((1, 3))\nprint(one_row_array) \n<\/pre><\/div>\n\n\n
Output:<\/strong><\/p>\n\n\n
\n[[1. 1. 1.]]\n<\/pre><\/div>\n\n\n
N x 1 array<\/strong><\/p>\n\n\n
\nimport numpy as np\n\none_col_array = np.ones((5, 1))\nprint(one_col_array) \n<\/pre><\/div>\n\n\n
Output:<\/strong><\/p>\n\n\n
\n[[1.]\n [1.]\n [1.]\n [1.]\n [1.]]\n<\/pre><\/div>\n\n\n
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1-dimensional int-type array<\/h3>\n\n\n
\nimport numpy as np\n\none_dim_int_array = np.ones(5, dtype=np.int64)\nprint(one_dim_int_array) \n<\/pre><\/div>\n\n\n
Output:<\/strong><\/p>\n\n\n
\n[1 1 1 1 1]\n<\/pre><\/div>\n\n\n
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2-dimensional int-type array<\/h3>\n\n\n
\nimport numpy as np\n\ntwo_dim_int_array = np.ones((3, 3), dtype=np.int64)\nprint(two_dim_int_array) \n<\/pre><\/div>\n\n\n
Output:<\/strong><\/p>\n\n\n
\n[[1 1 1]\n [1 1 1]\n [1 1 1]]\n<\/pre><\/div>\n\n\n
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1-dimensional custom data type array<\/h3>\n\n\n
\nimport numpy as np\n\ncustom_one_dim_array = np.ones(4, dtype=[('x', 'int'), ('y', 'float')])\nprint(custom_one_dim_array) \nprint(custom_one_dim_array.dtype) \n<\/pre><\/div>\n\n\n
Output:<\/strong><\/p>\n\n\n
\n[(1, 1.) (1, 1.) (1, 1.) (1, 1.)]\n[('x', '&lt;i4'), ('y', '&lt;f8')]\n<\/pre><\/div>\n\n\n
In this example, we specified the first value as an int and the second as a float.<\/p>\n\n\n\n
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2-dimensional custom data type array<\/h3>\n\n\n\n
We can specify the elements of the array as a tuple and also specify their data types.<\/p>\n\n\n
\nimport numpy as np\n\ncustom_two_dim_array = np.ones((2, 3), dtype=[('x', 'float'), ('y', 'int')])\nprint(custom_two_dim_array) \nprint(custom_two_dim_array.dtype) \n<\/pre><\/div>\n\n\n
Output:<\/strong><\/p>\n\n\n
\n[[(1., 1) (1., 1) (1., 1)]\n [(1., 1) (1., 1) (1., 1)]]\n[('x', '&lt;f8'), ('y', '&lt;i4')]\n<\/pre><\/div>\n\n\n
Here, the code specifies the first value of the tuple in the array elements to be a float and the second one to be an int.<\/p>\n\n\n\n
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Conclusion<\/h2>\n\n\n\n
That\u2019s all! In this tutorial, we learned about the Numpy ones<\/strong> method and practiced different types of examples using the same.
If you want to learn more about NumPy, feel free to go through our NumPy tutorials<\/a>.<\/p>\n\n\n\n
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Reference<\/h2>\n\n\n\n